Effect of a data collection threshold in the loss distribution approach

Mignola, Giulio; Ugoccioni, R.

doi:10.21314/jop.2006.019

Cited by 14 publications

(3 citation statements)

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“…Even if the impact is small often it should be estimated to justify the reporting level. Recent studies of this problem include Frachot et al [9], Bee [16], Chernobai et al [17], Mignola and Ugoccioni [18], Luo et al [19], and Baud et al [20]. A consistent procedure to adjust for missing data is to fit the data above the threshold using the correct conditional density.…”

Section: A Note On Modeling Truncated Datamentioning

confidence: 99%

Implementing loss distribution approach for operational risk

Shevchenko

2010

Appl Stoch Models Bus & Ind

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To quantify the operational risk capital charge under the current regulatory framework for banking supervision, referred to as Basel II, many banks adopt the Loss Distribution Approach. There are many modeling issues that should be resolved to use the approach in practice. In this paper we review the quantitative methods suggested in literature for implementation of the approach. In particular, the use of the Bayesian inference method that allows to take expert judgement and parameter uncertainty into account, modeling dependence and inclusion of insurance are discussed.

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Section: A Note On Modeling Truncated Datamentioning

confidence: 99%

Implementing loss distribution approach for operational risk

Shevchenko

2010

Appl Stoch Models Bus & Ind

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“…Moscadelli et al (2005) highlight the potential drawbacks of neglecting the existence of thresholds in the measurement process, suggesting that one way to circumvent this problem is to reconstruct the shape of the lower part of the distribution by fitting the collected data and extrapolating down to zero. Mignola and Ugoccioni (2007), on the other hand, argue that neglecting events below the loss data collection threshold does not lead to large errors in the aggregated expected loss quintiles and unexpected loss for threshold values up to fairly large percentiles of the severity distribution.…”

Section: Measuring Regulatory Capital Against Operational Riskmentioning

confidence: 99%

Operational Risk: A Survey

Moosa

2007

Financial Market

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Operational risk has, in a relatively short period of time, risen from non-recognition to prominence as the culprit for spectacular corporate collapses. This paper surveys the mushrooming literature on the subject, covering the definition, classification, characteristics, modeling and management of operational risk. It is concluded that operational risk is a controversial topic that will generate a significant amount of research in the years to come.

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“…Bee (2005)). The effect of data truncation in operational risk was studied in Baud, Frachot and Roncalli (2003), Chernobai et al (2005), Mignola and Ugoccioni (2006), and Luo et al (2007).…”

Section: Introductionmentioning

confidence: 99%

Modeling operational risk data reported above a time-varying threshold

Shevchenko¹,

Temnov²

2009

JOP

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Typically, operational risk losses are reported above a threshold. Fitting data reported above a constant threshold is a well known and studied problem. However, in practice, the losses are scaled for business and other factors before the fitting and thus the threshold is varying across the scaled data sample. A reporting level may also change when a bank changes its reporting policy. We present both the maximum likelihood and Bayesian Markov chain Monte Carlo approaches to fitting the frequency and severity loss distributions using data in the case of a time varying threshold. Estimation of the annual loss distribution accounting for parameter uncertainty is also presented.

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Effect of a data collection threshold in the loss distribution approach

Cited by 14 publications

References 0 publications

Implementing loss distribution approach for operational risk

Implementing loss distribution approach for operational risk

Operational Risk: A Survey

Modeling operational risk data reported above a time-varying threshold

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